Manufacturing systems are becoming more sophisticated and expensive, particularly with
the development of the intelligent industry. The complexity of the architecture and concept of …

The availability of computational power in the domain of Prognostics and Health
Management (PHM) with deep learning (DL) applications has attracted researchers …

Anomaly detection without employing dedicated sensors for each industrial machine is
recognized as one of the essential techniques for preventive maintenance and is especially …

The increase of productivity and decrease of production loss is an important goal for modern
industry to stay economically competitive. For that, efficient fault management and quick …

Purpose This study aims to bring awareness to the developing of fault detection systems
using the data collected from sensor devices/physical devices of various systems for …

The industrial application of data-driven methods for fault detection of new-design systems is
limited by the inevitable scarcity of real data. Physics-Informed Neural Networks (PINNs) can …

Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of
the components in a real system has been destroyed, and some anomalies appear so that …

In the last decade, the adoption of technological tools in manufacturing industry, such as the
use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the …

Maintenance is an activity that cannot be separated from the context of product
manufacturing. It is carried out to maintain the components' or machines' function so that no …

Predictive Maintenance (PdM) is the newest strategy for maintenance management in
industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected …